Feature identification and classification from aerial and satellite images have become increasingly valuable sources of information for natural resource management, and are useful in both public and private sectors for conservation and crop planning practices. However, current systems of forest inventorying are unsatisfactory, as they may be labor intensive, subject to substantial human error, or both.
Forest inventorying and analysis require the location and identification of valuable timber, typically present in stands of trees spread over large and geographically diverse areas. Current methods of locating and identifying timber include making field observations and measurements of individual trees, manually reviewing aerial photographs and satellite images, and automated or computer driven reviewing of digital aerial photographs and images. Field observation by professional foresters is both costly, labor-intensive, and slow, as it involves walking the forests and measuring the trees by hand. For example, up to 75% of a typical Appalachian region hardwood sawmill forestry staffs time may be spent looking for valuable timber. However, this labor-intensive search for timber usually meets only 70% of the sawmill's timber requirements. The remainder of the timber is then usually purchased in a closed bid auction. Accordingly, the sawmill thus incurs both the skilled labor costs involved with the manual location of timber, and premiums added to the cost of the timber purchased in the closed bid process, which can range from 30% to 100% of the timber's uncut fair market value. Tree measurements obtained by this method may also be substantially inaccurate because a forester cannot economically measure every tree or visit all parts of the tree stand measured. Rather, the forester will typically rely on sampling: measuring sample plots and generalizing the results to the whole stand, and then to the whole forest. This method is particularly subject to error in locating the more valuable tree stands, given the errors inherent in geographical sampling methods. Field measurement work is affected by the diverse methods that individual foresters use to make the measurements, as well as by the forester's exposure to adverse conditions such as weather changes and inhospitable terrain. These factors, among others, substantially affect the accuracy of timber inventories obtained by field observation methods.
Similarly, manual review of aerial photographs or images of a forest canopy, where comparisons to sample plots are sometimes used, also involves a time intensive review by a staff of skilled foresters. These methods are both inefficient and typically require a staff of foresters who are highly skilled in image interpretation. Moreover, variance in the level of experience and skill of the forester performing the review makes the analysis subject to human error and bias, and the accuracy of timber inventories obtained by this method is limited at best. Concurrent ground-truthing as a form of field observation to confirm or support the forester's analysis is often required, and even so, errors may lead to inaccuracies in tree count, stand location and composition, such that a less than satisfactory inventory is produced, whether for purchasing the timber, inventorying current timber, or conservation/ecological preservation of public and private lands.
Additionally, current systems and methods for automated digital image classification and analysis, when applied to images of forest canopies, such as tree stand and tree crown delineation, are also inaccurate or produce incomplete inventories. Typical algorithm based methods that rely on pixel color classification of imagery use only low-resolution imagery, and are accordingly inaccurate and incomplete at the stand level. Although pixel classification of satellite imagery may be used for forest inventories and estimating forest attributes, these are mainly appropriate only for large-scale forests, on the order of 100 hectares or more, and accordingly these methods fail to obtain accurate location or ownership information regarding the imaged forested areas. See Juha Hyyppa, et al., “A segmentation-based method to retrieve stem volume estimates from 3-dimensional tree height models produced by laser scanner,” Finnish Geodetic Institute, Department of Phogrammetry and Remote Sensing, MASALA, Finland. Moreover, typical methods using pixel classification for timber measurements at the stand-level is often unreliable and subject to error because the image resolution does not permit single tree measurements or accurately discern individual tree crown areas, i.e. the inventories obtained erroneously cluster small trees of limited total value may be erroneously classified as one large tree of substantial value.
Still other methods have been applied to high pulse-rate laser scans taken from aerial platforms, however current systems typically are only able to partially capture the physical dimensions of the trees captured. See Hyyppa, et al. High pulse-rate laser scanners digitally record the height of the tree canopy, enabling estimation of tree crown area. However, these images are in only a single narrow band or channel, have only grayscale values, and provide only estimates of tree crown area. Even when at high resolution, the computerized methods applied to these images are unable to accurately classify trees as to species, and species classifications would thus require ground truthing in order to provide accurate timber inventories. Accordingly, without tree species classification, there is a significant deficiency in the amount of information generated about the tree stand and forested region, and the value of the inventory to a potential land use planner, sawmill operator or land owner is significantly decreased.
Additional automated methods of image analysis that use rule-based processes to outline object boundaries have also been employed to determine the location of individual tree crowns based on small indentations in tree cluster boundaries, or identify and regroup segments of crown into single crowns. However, these methods only estimate the location of trees and estimate the area of tree crowns, and thus are not as accurate as actual identification of tree crowns. See Francois A. Gougeon and Donald G. Leckie, “Individual Tree Crown Image Analysis—A Step Towards Precision Forestry,” presented at the First Int. Precision Forestry Symposium, Seattle, Wash., USA (Jun. 17–20, 2001). For example, these methods typically only partially form polygons or boundaries around each tree crown, and estimate the remainder of the boundary using a flooding model methodology. “Comparison of Two Tree Apex Delineation Techniques,” International Forum Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, Pacific Forestry Centre, Victoria, British Columbia, Canada, pp. 93–104 (Feb. 10–12, 1998). Gougeon and Leckie have also described the valley following technique, which is used to delineate trees, but it has heretofore required a high degree of separation (e.g. shaded area) between individual trees in the tree stand in order to delineate individual trees. See Gougeon and Leckie (2001).
Moreover, none of the aforementioned automated or manual reviewing methods provides an efficient and accurate inventory that includes the actual economic value of the timberland being inventoried by taking into account the varying market value for trees of varying sizes and species, nor do they provide information on the ownership of the timberland being imaged and analyzed. Thus, there is a need in the art to provide a method for inventorying timberlands efficiently and accurately which provides valuation information such as the stem volume, size, species, location and ownership of particular tree stands and forests.